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📕 Table of Contents

💡 What is RAGFlow?

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.

🎮 Get Started

Try our cloud service at https://cloud.ragflow.io.

🔥 Latest Updates

🎉 Stay Tuned

⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟

🌟 Key Features

🍭 "Quality in, quality out"

🍱 Template-based chunking

🌱 Grounded citations with reduced hallucinations

🍔 Compatibility with heterogeneous data sources

🛀 Automated and effortless RAG workflow

🔎 System Architecture

🎬 Self-Hosting

📝 Prerequisites

Tip

If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.

🚀 Start up the server

  1. Ensure vm.max_map_count >= 262144:

    To check the value of vm.max_map_count:

    $ sysctl vm.max_map_count

    Reset vm.max_map_count to a value at least 262144 if it is not.

    In this case, we set it to 262144:

    $ sudo sysctl -w vm.max_map_count=262144

    This change will be reset after a system reboot. To ensure your change remains permanent, add or update thevm.max_map_count value in /etc/sysctl.conf accordingly:

  2. Clone the repo:
    $ git clone https://github.com/infiniflow/ragflow.git
  3. Start up the server using the pre-built Docker images:

Caution

All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64. If you are on an ARM64 platform, follow this guide to build a Docker image compatible with your system.

The command below downloads the v0.26.1 edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from v0.26.1, update the RAGFLOW_IMAGE variable accordingly in docker/.env before using docker compose to start the server.

$ cd ragflow/docker

git checkout v0.26.1

Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)

This step ensures the entrypoint.sh file in the code matches the Docker image version.

Use CPU for DeepDoc tasks:

$ docker compose -f docker-compose.yml up -d

To use GPU to accelerate DeepDoc tasks:

sed -i '1i DEVICE=gpu' .env

docker compose -f docker-compose.yml up -d

Note: Prior to v0.22.0, we provided both images with embedding models and slim images without embedding models. Details as follows:

RAGFlow image tag Image size (GB) Has embedding models? Stable?
v0.21.1 ≈9 ✔️ Stable release
v0.21.1-slim ≈2 ❌ Stable release

Starting with v0.22.0, we ship only the slim edition and no longer append the -slim suffix to the image tag.

  1. Check the server status after having the server up and running:
    $ docker logs -f docker-ragflow-cpu-1
    The following output confirms a successful launch of the system:
    / __ \ / | / _// // / _ __
    / /    / // /| | / / __ / /_ / // __ | | /| / /
    / , // ___ |/ // // _/ / // // /| |/ |/ /
    /_/ |    |/    / |    |__/// /_/ _/ |/|__/
  1. In your web browser, enter the IP address of your server and log in to RAGFlow.

    With the default settings, you only need to enter http://IP_OF_YOUR_MACHINE (sans port number) as the default HTTP serving port 80 can be omitted when using the default configurations.

  2. In service_conf.yaml.template, select the desired LLM factory in user_default_llm and update the API_KEY field with the corresponding API key.

    See llm_api_key_setup for more information.
    The show is on!

🔧 Configurations

When it comes to system configurations, you will need to manage the following files:

The ./docker/README file provides a detailed description of the environment settings and service configurations which can be used as ${ENV_VARS} in the service_conf.yaml.template file.

To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80to <YOUR_SERVING_PORT>:80.

Updates to the above configurations require a reboot of all containers to take effect:

$ docker compose -f docker-compose.yml up -d

Switch doc engine from Elasticsearch to Infinity

RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to Infinity, follow these steps:

  1. Stop all running containers:
    $ docker compose -f docker/docker-compose.yml down -v

Warning

-v will delete the docker container volumes, and the existing data will be cleared.

  1. Set DOC_ENGINE in docker/.env to infinity.
  2. Start the containers:
    $ docker compose -f docker-compose.yml up -d

Warning

Switching to Infinity on a Linux/arm64 machine is not yet officially supported.

🔧 Build a Docker image

This image is approximately 2 GB in size and relies on external LLM and embedding services.

git clone https://github.com/infiniflow/ragflow.git cd ragflow/ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .

Or if you are behind a proxy, you can pass proxy arguments:

docker build --platform linux/amd64
--build-arg http_proxy=http://YOUR_PROXY:PORT
--build-arg https_proxy=http://YOUR_PROXY:PORT
-f Dockerfile -t infiniflow/ragflow:nightly .

🔨 Launch service from source for development

  1. Install uv and pre-commit, or skip this step if they are already installed:
    pipx install uv pre-commit
  2. Clone the source code and install Python dependencies:
    git clone https://github.com/infiniflow/ragflow.git
    cd ragflow/
    uv sync --python 3.13 # install RAGFlow dependent python modules
    uv run python3 download_deps.py
    pre-commit install
  3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
    docker compose -f docker/docker-compose-base.yml up -d
    Add the following line to /etc/hosts to resolve all hosts specified in docker/.env to 127.0.0.1:
127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
  1. If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:
    export HF_ENDPOINT=https://hf-mirror.com
  2. If your operating system does not have jemalloc, please install it as follows:

Ubuntu

sudo apt-get install libjemalloc-dev

CentOS

sudo yum install jemalloc

OpenSUSE

sudo zypper install jemalloc

macOS

sudo brew install jemalloc 6. Launch backend service:
source .venv/bin/activate
export PYTHONPATH=$(pwd)
bash docker/launch_backend_service.sh 7. Install frontend dependencies: 8. Launch frontend service:
The following output confirms a successful launch of the system:
9. Stop RAGFlow front-end and back-end service after development is complete:
pkill -f "ragflow_server.py|task_executor.py"

📚 Documentation

📜 Roadmap

See the RAGFlow Roadmap 2026

🏄 Community

🙌 Contributing

RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our Contribution Guidelines first.